Multi-objective optimization design for a battery pack of electric vehicle with surrogate models

In this investigation, a systematic surrogate-based optimization design framework for a battery pack is presented. An air-cooling battery pack equipped on electric vehicles is first designed. Finite element analysis (FEA) results of the baseline design show that global maximum stresses under x-axis and y-axis transient acceleration shock condition are both above the tensile limit of material. Selecting the panel and beam thickness of battery pack as design variables, with global maximum stress constraints in shock cases, a multi-objective optimization problem is implemented using metamodel technique and multi-objective particle-swarm-optimization (MOPSO) algorithm to simultaneously minimize the total mass and maximize the restrained basic frequency. It is found that 2nd order polynomial response surface (PRS), 3rd order PRS and radial basis function (RBF) are the most accurate and appropriate metamodels for restrained basic frequency, global maximum stresses under x-axis and y-axis shock conditions respectively. Results demonstrate that all the optimal solutions in Pareto Frontier have heavier weight and lower frequency compared with baseline design due to the restriction of global maximum stress response. Finally, two optimal schemes, “Knee Point” and “lightest weight”, satisfied both of the stress constraint conditions, show great consistency with FEA results and can be selected as alternative improved schemes

Robust Topology Optimization Based on Stochastic Collocation Methods under Loading Uncertainties

A robust topology optimization (RTO) approach with consideration of loading uncertainties is developed in this paper. The stochastic collocation method combined with full tensor product grid and Smolyak sparse grid transforms the robust formulation into a weighted multiple loading deterministic problem at the collocation points. The proposed approach is amenable to implementation in existing commercial topology optimization software package and thus feasible to practical engineering problems. Numerical examples of two- and three-dimensional topology optimization problems are provided to demonstrate the proposed RTO approach and its applications. The optimal topologies obtained from deterministic and robust topology optimization designs under tensor product grid and sparse grid with different levels are compared with one another to investigate the pros and cons of optimization algorithm on final topologies, and an extensive Monte Carlo simulation is also performed to verify the proposed approach

Atmospheric chemistry surrogate modeling with sparse identification of nonlinear dynamics

Modeling atmospheric chemistry is computationally expensive and limits the widespread use of atmospheric chemical transport models. This computational cost arises from solving high-dimensional systems of stiff differential equations. Previous work has demonstrated the promise of machine learning (ML) to accelerate air quality model simulations but has suffered from numerical instability during long-term simulations. This may be because previous ML-based efforts have relied on explicit Euler time integration -- which is known to be unstable for stiff systems -- and have used neural networks which are prone to overfitting. We hypothesize that the creation of parsimonious models combined with modern numerical integration techniques can overcome this limitation. Using a small-scale photochemical mechanism to explore the potential of these methods, we have created a machine-learned surrogate by (1) reducing dimensionality using singular value decomposition to create an interpretably-compressed low-dimensional latent space, and (2) using Sparse Identification of Nonlinear Dynamics (SINDy) to create a differential-equation-based representation of the underlying chemical dynamics in the compressed latent space with reduced numerical stiffness. The root mean square error of the ML model prediction for ozone concentration over nine days is 37.8% of the root mean concentration across all simulations in our testing dataset. The surrogate model is 11$\times$ faster with 12$\times$ fewer integration timesteps compared to the reference model and is numerically stable in all tested simulations. Overall, we find that SINDy can be used to create fast, stable, and accurate surrogates of a simple photochemical mechanism. In future work, we will explore the application of this method to more detailed mechanisms and their use in large-scale simulations

Uncertainty Quantification in Reduced-Order Gas-Phase Atmospheric Chemistry Modeling using Ensemble SINDy

Uncertainty quantification during atmospheric chemistry modeling is computationally expensive as it typically requires a large number of simulations using complex models. As large-scale modeling is typically performed with simplified chemical mechanisms for computational tractability, we describe a probabilistic surrogate modeling method using principal components analysis (PCA) and Ensemble Sparse Identification of Nonlinear Dynamics (E-SINDy) to both automatically simplify a gas-phase chemistry mechanism and to quantify the uncertainty introduced when doing so. We demonstrate the application of this method on a small photochemical box model for ozone formation. With 100 ensemble members, the calibration $R$-squared value is 0.96 among the three latent species on average and 0.98 for ozone, demonstrating that predicted model uncertainty aligns well with actual model error. In addition to uncertainty quantification, this probabilistic method also improves accuracy as compared to an equivalent deterministic version, by $\sim$60% for the ensemble prediction mean or $\sim$50% for deterministic prediction by the best-performing single ensemble member. Overall, the ozone testing root mean square error (RMSE) is 15.1% of its root mean square (RMS) concentration. Although our probabilistic ensemble simulation ends up being slower than the reference model it emulates, we expect that use of a more complex reference model in future work will result in additional opportunities for acceleration. Versions of this approach applied to full-scale chemical mechanisms may result in improved uncertainty quantification in models of atmospheric composition, leading to enhanced atmospheric understanding and improved support for air quality control and regulation

Tree based Progressive Regression Model for Watch-Time Prediction in Short-video Recommendation

An accurate prediction of watch time has been of vital importance to enhance user engagement in video recommender systems. To achieve this, there are four properties that a watch time prediction framework should satisfy: first, despite its continuous value, watch time is also an ordinal variable and the relative ordering between its values reflects the differences in user preferences. Therefore the ordinal relations should be reflected in watch time predictions. Second, the conditional dependence between the video-watching behaviors should be captured in the model. For instance, one has to watch half of the video before he/she finishes watching the whole video. Third, modeling watch time with a point estimation ignores the fact that models might give results with high uncertainty and this could cause bad cases in recommender systems. Therefore the framework should be aware of prediction uncertainty. Forth, the real-life recommender systems suffer from severe bias amplifications thus an estimation without bias amplification is expected. Therefore we propose TPM for watch time prediction. Specifically, the ordinal ranks of watch time are introduced into TPM and the problem is decomposed into a series of conditional dependent classification tasks which are organized into a tree structure. The expectation of watch time can be generated by traversing the tree and the variance of watch time predictions is explicitly introduced into the objective function as a measurement for uncertainty. Moreover, we illustrate that backdoor adjustment can be seamlessly incorporated into TPM, which alleviates bias amplifications. Extensive offline evaluations have been conducted in public datasets and TPM have been deployed in a real-world video app Kuaishou with over 300 million DAUs. The results indicate that TPM outperforms state-of-the-art approaches and indeed improves video consumption significantly

Discrete Conditional Diffusion for Reranking in Recommendation

Reranking plays a crucial role in modern multi-stage recommender systems by rearranging the initial ranking list to model interplay between items. Considering the inherent challenges of reranking such as combinatorial searching space, some previous studies have adopted the evaluator-generator paradigm, with a generator producing feasible sequences and a evaluator selecting the best one based on estimated listwise utility. Inspired by the remarkable success of diffusion generative models, this paper explores the potential of diffusion models for generating high-quality sequences in reranking. However, we argue that it is nontrivial to take diffusion models as the generator in the context of recommendation. Firstly, diffusion models primarily operate in continuous data space, differing from the discrete data space of item permutations. Secondly, the recommendation task is different from conventional generation tasks as the purpose of recommender systems is to fulfill user interests. Lastly, real-life recommender systems require efficiency, posing challenges for the inference of diffusion models. To overcome these challenges, we propose a novel Discrete Conditional Diffusion Reranking (DCDR) framework for recommendation. DCDR extends traditional diffusion models by introducing a discrete forward process with tractable posteriors, which adds noise to item sequences through step-wise discrete operations (e.g., swapping). Additionally, DCDR incorporates a conditional reverse process that generates item sequences conditioned on expected user responses. Extensive offline experiments conducted on public datasets demonstrate that DCDR outperforms state-of-the-art reranking methods. Furthermore, DCDR has been deployed in a real-world video app with over 300 million daily active users, significantly enhancing online recommendation quality

Reliability-Based Topology Optimization Using Stochastic Response Surface Method with Sparse Grid Design

A mathematical framework is developed which integrates the reliability concept into topology optimization to solve reliability-based topology optimization (RBTO) problems under uncertainty. Two typical methodologies have been presented and implemented, including the performance measure approach (PMA) and the sequential optimization and reliability assessment (SORA). To enhance the computational efficiency of reliability analysis, stochastic response surface method (SRSM) is applied to approximate the true limit state function with respect to the normalized random variables, combined with the reasonable design of experiments generated by sparse grid design, which was proven to be an effective and special discretization technique. The uncertainties such as material property and external loads are considered on three numerical examples: a cantilever beam, a loaded knee structure, and a heat conduction problem. Monte-Carlo simulations are also performed to verify the accuracy of the failure probabilities computed by the proposed approach. Based on the results, it is demonstrated that application of SRSM with SGD can produce an efficient reliability analysis in RBTO which enables a more reliable design than that obtained by DTO. It is also found that, under identical accuracy, SORA is superior to PMA in view of computational efficiency

Enhanced transcriptomic resilience following increased alternative splicing and differential isoform production between air pollution conurbations

Adversehealth outcomes caused by ambient particulate matter (PM) pollution occur in a 16progressive process, with neutrophils eliciting inflammation or pathogenesis. We investigated the 17toxico-transcriptomic mechanisms of PM in real-life settings by comparing healthy residents living 18in Beijing and Chengde, the opposing ends of a well-recognised air pollution (AP) corridor in China. 19Beijing recruits (BRs) uniquelyexpressed ~12,000 alternativesplicing (AS)-derived transcripts, 20largely elevating the proportion of transcripts significantly correlated with PM concentration. BRs 21expressed PM-associated isoforms (PMAIs) of PFKFB3and LDHA,encoding enzymes responsible 22for stimulatingand maintaining glycolysis. PMAIsof PFKFB3featured different COOH-terminals, 23targeting PFKFB3 to different sub-cellular functional compartments and stimulating glycolysis. 24PMAIs of LDHAhavelonger 3’UTRs relative to those expressed in Chengderecruits (CRs),allowing 25glycolysis maintenance by enhancing LDHAmRNA stability and translational efficiency. PMAIs 26weredirectly regulated by different HIF-1Aand HIF-1Bisoforms. BRs expressed more non-func-27tional Fasisoforms and a resultant reduction of intact Fasproportion is expectedto inhibit the trans-28mission of apoptotic signals and prolong neutrophil lifespan. BRs expressed both membrane-bound 29and soluble IL-6Risoforms insteadof only one in CRs. The presence of both IL-6Risoforms sug-30gested a higher migration capacity of neutrophils in BRs. PMAIs of HIF-1Aand PFKFB3were down-31regulated inChronic Obstructive Pulmonary Disease patients compared with BRs, implying HIF-1 32mediated defective glycolysis may mediate neutrophil dysfunction. PMAIs could explain large var-33iances of different phenotypes, highlighting their potential application as biomarkers and therapeu-34tic targets in PM-induced diseases, which remain poorly elucidated

The Euscaphis japonica genome and the evolution of malvids

Malvids is one of the largest clades of rosids, includes 58 families and exhibits remarkable morphological and ecological diversity. Here, we report a high-quality chromosome-level genome assembly for Euscaphis japonica, an early-diverging species within malvids. Genome-based phylogenetic analysis suggests that the unstable phylogenetic position of E. japonica may result from incomplete lineage sorting and hybridization event during the diversification of the ancestral population of malvids. Euscaphis japonica experienced two polyploidization events: the ancient whole genome triplication event shared with most eudicots (commonly known as the c event) and a more recent whole genome duplication event, unique to E. japonica. By resequencing 101 samples from 11 populations, we speculate that the temperature has led to the differentiation of the evergreen and deciduous of E. japonica and the completely different population histories of these two groups. In total, 1012 candidate positively selected genes in the evergreen were detected, some of which are involved in flower and fruit development. We found that reddening and dehiscence of the E. japonica pericarp and long fruit-hanging time promoted the reproduction of E. japonica populations, and revealed the expression patterns of genes related to fruit reddening, dehiscence and abscission. The key genes involved in pentacyclic triterpene synthesis in E. japonica were identified, and different expression patterns of these genes may contribute to pentacyclic triterpene diversification. Our work sheds light on the evolution of E. japonica and malvids, particularly on the diversification of E. japonica and the genetic basis for their fruit dehiscence and abscission.DATA AVAILABILITY STATEMENT : All sequences described in this manuscript have been submitted to the National Genomics Data Center (NGDC). The raw whole-genome data of E. japonica have been deposited in BioProject/GSA (https://bigd.big.ac.cn/gsa.) under the accession codes PRJCA005268/CRA004271, and the assembly and annotation data have been deposited at BioProject/GWH (https://bigd.big.ac.cn/gwh) under the accession codes PRJCA005268/GWHBCHS00000000. The raw transcriptomes data of E. japonica have been deposited in BioProject/GSA (https://bigd.big.ac.cn/gsa.) under the accession codes PRJCA005298/CRA004272.SUPPLEMENTARY MATERIAL 1: Supplementary Note 1. Chromosome number assessment. Supplementary Note 2. Whole-genome duplication identification and dating. Supplementary Note 3. Observation of E. japonica seed dispersal. Supplementary Note 4. Determination of pentacyclic triterpene substances. Figure S1. Cytogenetic analysis of E. japonica. Figure S2. Genome size and heterozygosity of E. japonica estimation using 17 k-mer distribution. Figure S3. Interchromosomal of Hi-C chromosome contact map of E. japonica genome. Figure S4. Gene structure prediction results of E. japonica and other species. Figure S5. Venn diagram shows gene families of malvids. Figure S6. Phylogenetic tree constructed by chloroplast genomes from 17 species. Figure S7. Concatenated- and ASTRAL-based phylogenetic trees. Figure S8. Ks distribution in E. japonica. Figure S9. Distributions of synonymous substitutions per synonymous site (Ks) of one-to-one orthologs identified between E. japonica and P. trichocarpa and V. vinifera. Figure S10. Population structure plot. Figure S11. Fixation index (FST) heat map among E. japonica populations. Figure S12. Phylogenetic analysis of MADS-box genes from O. sativa, A. thaliana, E. japonica, and T. cacao. Figure S13. Observation the fruit development. Figure S14. Animal seed dispersal. Figure S15. Anthocyanin biosynthesis in E. japonica fruits. Figure S16. Carotenoid accumulation and the chlorophyll degradation in E. japonica fruits. Figure S17. Expression profile of fruit dehiscence-related genes. Figure S18. Phylogenetic tree of DELLA genes obtained from six malvids species. Figure S19. Phylogenetic tree of CAD genes obtained from seven malvids species. Figure S20. Expression pattern of fruit abscission-related genes. Figure S21. Structure of pentacyclic triterpene compounds separated from Euscaphis. Figure S22. Phylogenetic tree of HMGR gene in plants. Figure S23. Phylogenetic tree of P450s gene family obtained from A. thaliana and E. japonica.SUPPLEMENTARY MATERIAL 2: Table S1. Assembled statistics of E. japonica genome. Table S2. Evaluation of E. japonica genome assembly. Table S3. Chromosome length of E. japonica. Table S4. Prediction of gene structures of the E. japonica genome. Table S5. Statistics on the function annotation of the E. japonica genome. Table S6. Non-coding RNA annotation results of E. japonica genome. Table S7. BUSCO assessment of the E. japonica annotated genome. Table S8. Statistic of repeat sequence in E. japonica genome. Table S9. Gene-clustering statistics for 17 species. Table S10. KEGG enrichment result of unique genes families of E. japonica. Table S11. Gene Ontology (GO) and KEGG enrichment result of significant shared by malvids species gene families. Table S12. Gene Ontology (GO) and KEGG enrichment result of significant expansion of E. japonica gene families. Table S13. Gene Ontology (GO) enrichment result of significant contraction of E. japonica gene families. Table S14. Statistical sampling population information. Table S15. Statistics population resequencing information. Table S16. Statistical nucleotide polymorphisms in the populations. Table S17. Candidate positive selection genes (PSGs) in the evergreen population. Table S18. Candidate positive selection genes (PSGs) in the deciduous population. Table S19. Gene Ontology (GO) enrichment result of significant PSGs in the evergreen population. Table S20. List of MADS-box genes identified in E. japonica. Table S21. Genes involved in anthocyanin biosynthesis, carotenoid biosynthesis, and chlorophyll degradation. Table S22. Identification fruit dehiscence-related genes in E. japonica. Table S23. Genes related to lignin synthesis that are highly expressed during pericarp dehiscence. Table S24. Gene expression levels (FPKMs) of fruit abscission-related genes in pericarp. Table S25. Triterpene compounds separated from Euscaphis. Table S26. Number of putative pentacyclic triterpene-related genes in the malvids species. Table S27. Identified pentacyclic triterpene synthesis-related genes in E. japonica genome. Table S28. Statistical simple sequence repeat.Fund for Excellent Doctoral Dissertation of Fujian Agriculture and Forestry University, China; Fujian Provincial Department of Science E. japonica Evolution and Selection of Ornamental Medicinal Resources, China; the Project of Forestry Peak Discipline at Fujian Agriculture and Forestry University, China; the Collection, Development and Utilization of Eascaphis konlshli Germplasm Resources; the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program and from Ghent University.https://onlinelibrary.wiley.com/journal/1365313xam2022BiochemistryGeneticsMicrobiology and Plant Patholog